PS216-PS218_roots-shoots
zagor
2025-09-29
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 555542 29.7 1244034 66.5 686457 36.7
## Vcells 1022955 7.9 8388608 64.0 1876634 14.42 palette
my_dir = file.path("..", "reports", "Table7")
if (!dir.exists(my_dir)) {
dir.create(my_dir)
}
fr = file.path(my_dir, 'Tests.txt')
file.create(fr)## [1] TRUE3 f-ctions
3.1 summary stat
summary_stats <- function(df, measurevar, groupvars, conf.level = 0.95) {
df %>%
dplyr::group_by(across(all_of(groupvars))) %>%
dplyr::summarise(
N = sum(!is.na(.data[[measurevar]])),
mean = mean(.data[[measurevar]], na.rm = TRUE),
sd = sd(.data[[measurevar]], na.rm = TRUE),
.groups = "drop"
) %>%
dplyr::mutate(
se = sd / sqrt(N),
ci = se * qt(conf.level/2 + 0.5, N - 1)
)
}3.2 scale PS
process_data <- function(df, groupvars, measurevar, scale_treatment = "noninoculated") {
df.long = df %>%
tidyr::pivot_longer(cols = 3:ncol(df), names_to = "transcript",
values_to = measurevar, values_drop_na = TRUE)
data.SE = summary_stats(df.long, measurevar, groupvars)
scale_reference = data.SE %>%
dplyr::filter(Treatment == scale_treatment) %>%
dplyr::select(Tissue, transcript, mean) %>%
dplyr::rename(scale_mean = mean)
df.scaled <- dplyr::left_join(df.long, scale_reference, by = c("Tissue", "transcript")) %>%
dplyr::mutate(scaled = .data[[measurevar]] / scale_mean) %>%
dplyr::select(-scale_mean)
df.scaled = df.scaled %>%
dplyr::arrange(dplyr::desc(transcript), dplyr::desc(Treatment))
shoots = df.scaled %>% dplyr::filter(Tissue == "shoots")
roots = df.scaled %>% dplyr::filter(Tissue == "roots")
return(list(
shoots = shoots,
roots = roots
))
}3.3 perm t
perm_test_by_transcript <- function(mydata.long, measurevar = "measurement", groupvar = "Treatment") {
temp = data.frame()
transcript_levels = levels(mydata.long$transcript)
treatment_levels = levels(mydata.long[[groupvar]])
treatment_pairs = combn(treatment_levels, 2, simplify = FALSE)
for(i in transcript_levels) {
data = mydata.long[mydata.long$transcript == i, ]
k = 8 # nrow(data)
pvalues = purrr::map_dbl(treatment_pairs, function(x) {
subset_data = base::subset(data, data[[groupvar]] %in% x)
res = MKinfer::perm.t.test(
formula = stats::as.formula(base::paste(measurevar, "~", groupvar)),
data = subset_data,
alternative = "two.sided",
mu = 0,
paired = FALSE,
var.equal = FALSE,
conf.level = 0.95,
perm.conf.int = 0.95,
symmetric = TRUE,
p.adjust.method = "holm",
detailed = TRUE,
R = choose(k, k/2), # sum(choose(k, 1:(k-1))),
set.seed = 123456
)
res$perm.p.value
})
tmp = base::as.data.frame(base::t(pvalues))
colnames(tmp) = purrr::map_chr(treatment_pairs, ~base::paste(.x, collapse = ' vs '))
rownames(tmp) = i
temp <- base::rbind(temp, tmp)
}
return(temp)
}3.4 plot
plot_gene_expression <- function(data.SE
, data.long
, stat.test.sig
, transcripts_excl = c('13-LOX', 'PTI5')
, color_values
, facet_cols = 6
, y_scales = NULL
, dodge_width = 0.8,
plot_title = ""
) {
dodge = position_dodge(width = dodge_width)
data.SE.filtered = dplyr::filter(data.SE, !transcript %in% transcripts_excl)
data.long.filtered = dplyr::filter(data.long, !transcript %in% transcripts_excl)
stat.test.filtered = NULL
if (nrow(stat.test.sig) > 0) stat.test.filtered = dplyr::filter(stat.test.sig, !transcript %in% transcripts_excl)
p = ggplot(data.SE.filtered, aes(x = Treatment, y = mean), colour = "black") +
geom_point(size=3.5, shape = 22, position = dodge, aes(fill = Treatment), colour = "black") +
geom_point(data = data.long.filtered, aes(x = Treatment, y = scaled, fill = Treatment), colour = "black",
size = 2.0, shape = 21,
position = dodge) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.3, lwd = 0.5,
position = dodge) +
facet_wrap(~ transcript, ncol = facet_cols, scales = "free", drop = TRUE)
if (!is.null(y_scales)) {
p = p + ggh4x::facetted_pos_scales(y = y_scales)
}
p = p +
# geom_hline(yintercept = 2.5, alpha = 0.0) +
geom_hline(yintercept = 0, alpha = 0.0) +
geom_hline(yintercept = 1, alpha = 0.5, linetype = "dotted", col = "gray45") +
ggtitle(plot_title) +
theme_bw() +
scale_colour_manual(name = "", values = rev(color_values)) +
scale_fill_manual(name = "", values = rev(color_values)) +
labs(x = "", y = "Relative gene expression (+/- SE)"
# , subtitle = "Permutation t-test measurements"
) +
theme(plot.subtitle = element_text(size = 10),
axis.text = element_text(size = 12.5),
axis.text.x = element_text(size = 12.5, angle = 90),
axis.title = element_text(size = 12.5, face = "bold"),
strip.text = element_text(size = 12.5),
title = element_text(size = 15, face = "bold"),
# axis.ticks.x = element_blank(),
legend.key.height = unit(1.5, "cm"),
legend.key.width = unit(1.75, "cm"),
legend.text = element_text(size = 12.5),
legend.title = element_text(size = 12.5),
legend.background = element_rect(fill = "transparent", size = 0.5, linetype = "dotted"),
legend.position = "top",
legend.justification = "right",
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.y.right = element_blank(),
axis.text.y.right = element_blank(),
# axis.ticks.y = element_blank(),
axis.text.y = element_text(size = 12.5, margin = margin(r = 0)),
panel.spacing = unit(1, "lines"),
strip.background = element_rect(size = 0.5, fill = "transparent", color = NA) ,
panel.border = element_blank(),
axis.line = element_line(color = "black")
)
p = p + theme(legend.position = "none")
if (!is.null(stat.test.filtered) && nrow(stat.test.filtered) > 0) {
p = p + ggpubr::stat_pvalue_manual(
stat.test.filtered,
label = "p.adj.signif",
xmin = "xmin",
xmax = "xmax",
y.position = "y.position",
hide.ns = FALSE,
tip.length = 0.01,
step.increase = 0.075,
inherit.aes = FALSE
)
}
return(p)
}3.5 effects
Denote: PR1B has LOQ values
dens_and_effect <- function(mydata.long, p_colors) {
# Plot density with x="measurement"
p1 = ggpubr::ggdensity(mydata.long, x = "measurement",
add = "mean", rug = TRUE,
color = "Treatment", fill = "Treatment",
facet.by = 'transcript') +
scale_fill_manual(name = "", values = rev(p_colors)) +
scale_color_manual(name = "", values = rev(p_colors)) +
ggtitle("measurement") +
facet_wrap(~transcript, ncol = 4, scales = "free")
print(p1)
# Plot density with x="scaled"
p2 = ggpubr::ggdensity(mydata.long, x = "scaled",
add = "mean", rug = TRUE,
color = "Treatment", fill = "Treatment",
facet.by = 'transcript') +
scale_fill_manual(name = "", values = rev(p_colors)) +
scale_color_manual(name = "", values = rev(p_colors)) +
ggtitle("scaled") +
facet_wrap(~transcript, ncol = 4, scales = "free")
print(p2)
cat(crayon::red('Shapiro-Wilk Normality Test\n'))
# Shapiro-Wilk normality test on measurement
norm_meas = mydata.long %>%
dplyr::select(-Tissue, -Treatment, -scaled) %>%
tidyr::gather(key = "transcript", value = "measurement") %>%
dplyr::group_by(transcript) %>%
dplyr::do(broom::tidy(shapiro.test(.$measurement))) %>%
dplyr::ungroup() %>%
dplyr::mutate(p.adj = p.adjust(p.value, method = "holm")) %>% # or "bonferroni", "BH"
dplyr::select(transcript, W = statistic, p.value, p.adj)
print(norm_meas)
# Shapiro-Wilk normality test on scaled
norm_scaled = mydata.long %>%
dplyr::select(-Tissue, -Treatment, -measurement) %>%
tidyr::gather(key = "transcript", value = "scaled") %>%
dplyr::group_by(transcript) %>%
dplyr::do(broom::tidy(shapiro.test(.$scaled))) %>%
dplyr::ungroup() %>%
dplyr::mutate(p.adj = p.adjust(p.value, method = "holm")) %>% # or "bonferroni", "BH"
dplyr::select(transcript, W = statistic, p.value, p.adj)
print(norm_scaled)
cat(crayon::red("Quantile-Quantile plots\n"))
plot_list = mydata.long %>%
dplyr::group_by(transcript) %>%
dplyr::group_map(~ {
ggpubr::ggqqplot(.x$measurement) +
ggplot2::ggtitle(paste("", .y$transcript))
})
ggpubr::ggarrange(plotlist = plot_list, ncol = 4, nrow = ceiling(length(plot_list) / 4))
cat(crayon::red("Levene's test for homogeneity of variance across groups\n"))
# Levene's test on measurement
lev_meas = mydata.long %>%
dplyr::group_by(transcript) %>%
rstatix::levene_test(measurement ~ Treatment)
print(lev_meas)
# Levene's test on scaled
lev_scaled = mydata.long %>%
dplyr::group_by(transcript) %>%
rstatix::levene_test(scaled ~ Treatment)
print(lev_scaled)
cat(crayon::red("Wilcoxon effect size\n"))
# Wilcoxon effect size on measurement
eff_meas = mydata.long %>%
dplyr::group_by(transcript) %>%
rstatix::wilcox_effsize(measurement ~ Treatment)
print(eff_meas)
# Wilcoxon effect size on scaled
eff_scaled = mydata.long %>%
dplyr::group_by(transcript) %>%
rstatix::wilcox_effsize(scaled ~ Treatment)
print(eff_scaled)
cat(crayon::red("Cohen's d Measure of Effect Size\n"))
# Cohen's d on measurement
coh_meas = mydata.long %>%
dplyr::group_by(transcript) %>%
rstatix::cohens_d(measurement ~ Treatment, paired = FALSE)
print(coh_meas)
# Cohen's d on scaled
coh_scaled = mydata.long %>%
dplyr::group_by(transcript) %>%
rstatix::cohens_d(scaled ~ Treatment, paired = FALSE)
print(coh_scaled)
invisible(list(
p_scaled = p1,
p_measurement = p2,
shapiro_measurement = norm_meas,
shapiro_scaled = norm_scaled,
levene_measurement = lev_meas,
levene_scaled = lev_scaled,
wilcoxon_measurement = eff_meas,
wilcoxon_scaled = eff_scaled,
cohensd_measurement = coh_meas,
cohensd_scaled = coh_scaled
))
}3.6 test and plot
test_and_plot <- function(data_long_raw
, myorder
, pal
, what
, plot_gene_expression_func
, groupvars = c("Treatment", "transcript")
, y_scales1
, y_scales2
) {
mydata.long = dplyr::as_tibble(data.table::data.table(data_long_raw))
mydata.long$transcript = factor(mydata.long$transcript, levels = myorder)
mydata.long = mydata.long %>% dplyr::arrange(factor(transcript, levels = myorder))
mydata.long.SE = summary_stats(mydata.long, measurevar = "scaled", groupvars = groupvars)
results = dens_and_effect(mydata.long, p_colors = pal)
cat(what, file = fr, append = TRUE, sep = "\n")
cat("shapiro", file = fr, append = TRUE, sep = "\n")
output_text = capture.output(as.data.frame(results$shapiro_measurement))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("levene", file = fr, append = TRUE, sep = "\n")
output_text = capture.output(as.data.frame(results$levene_measurement))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("wilcoxon", file = fr, append = TRUE, sep = "\n")
output_text = capture.output(as.data.frame(results$wilcoxon_measurement))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("cohensd", file = fr, append = TRUE, sep = "\n")
output_text = capture.output(as.data.frame(results$cohensd_measurement))
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
temp = perm_test_by_transcript(mydata.long, measurevar = "measurement", groupvar = "Treatment")
temp$transcript = rownames(temp)
perm = tidyr::gather(temp, contrast, perm.p, colnames(temp)[1]:colnames(temp)[ncol(temp)-1], factor_key=TRUE)
perm$group1 = gsub(' vs.*', '', perm$contrast)
perm$group2 = gsub('.* vs ', '', perm$contrast)
perm$perm.p.adj = p.adjust(perm$perm.p, method = 'BH')
perm$perm.p.adj.signif = 'ns'
perm$perm.p.adj.signif[perm$perm.p.adj < 0.0001] = '**'
perm$perm.p.adj.signif[perm$perm.p.adj < 0.001] = '**'
perm$perm.p.adj.signif[perm$perm.p.adj < 0.05] = '*'
# like rstatix object
stat.test = perm %>%
dplyr::select(transcript, group1, group2, perm.p, perm.p.adj, perm.p.adj.signif) %>%
dplyr::rename(
p = perm.p,
p.adj = perm.p.adj,
p.adj.signif = perm.p.adj.signif
)
y_pos_df = mydata.long %>%
dplyr::group_by(transcript, Treatment) %>%
dplyr::summarise(max_scaled = max(scaled, na.rm = TRUE)) %>%
dplyr::group_by(transcript) %>%
dplyr::summarise(y.position = max(max_scaled) * 1.0)
stat.test = stat.test %>%
dplyr::left_join(y_pos_df, by = "transcript")
stat.test = stat.test %>%
dplyr::mutate(
xmin = as.numeric(factor(group1, levels = unique(mydata.long$Treatment))),
xmax = as.numeric(factor(group2, levels = unique(mydata.long$Treatment)))
)
stat.test.sig <- stat.test %>%
dplyr::filter(p.adj < 0.05)
group1 = sapply(y_scales1, function(x) {
s = deparse(x)
s_full = paste(s, collapse = " ")
sub('.*transcript == *"([^"]+)".*', '\\1', s_full)
})
group2 = sapply(y_scales2, function(x) {
s = deparse(x)
s_full = paste(s, collapse = " ")
sub('.*transcript == *"([^"]+)".*', '\\1', s_full)
})
p1 = plot_gene_expression_func(
data.SE = mydata.long.SE,
data.long = mydata.long,
stat.test.sig = stat.test.sig,
transcripts_excl = group2,
facet_cols = 2,
color_values = pal,
plot_title = what,
y_scales = y_scales1,
dodge_width = 0.8
)
print(p1)
p2 = plot_gene_expression_func(
data.SE = mydata.long.SE,
data.long = mydata.long,
stat.test.sig = stat.test.sig,
transcripts_excl = group1,
facet_cols = 6,
color_values = pal,
plot_title = what,
y_scales = y_scales2,
dodge_width = 0.8
)
print(p2)
return(list(plot1 = p1, plot2 = p2, stat.test = stat.test))
}4 Test
4.1 input
## [1] "README.md" "Table S1.xlsx" "Table S2.xlsx" "Table S7.xlsx"fn = 'Table S7.xlsx'
PS216 = openxlsx::read.xlsx(xlsxFile = file.path(fp, fn),
sheet = 'Test',
startRow = 1,
colNames = TRUE,
rowNames = FALSE,
detectDates = FALSE,
skipEmptyRows = TRUE,
skipEmptyCols = TRUE,
rows = NULL,
cols = NULL,
check.names = FALSE,
sep.names = ".",
namedRegion = NULL,
na.strings = "NA",
fillMergedCells = FALSE)
data.table::setDT(PS216)
PS216[, Sample.ID := NULL]
PS216[, Genotype := NULL]
PS216$Tissue = as.factor(trimws(PS216$Tissue))
PS216$Treatment = factor(trimws(PS216$Treatment), levels = c("noninoculated", "inoculated"))
PS218 = PS216[grep('PS-216', PS216$Strain, invert = TRUE), ]
PS216 = PS216[grep('PS-218', PS216$Strain, invert = TRUE), ]
PS216[, Strain := NULL]
PS218[, Strain := NULL]4.2 params
4.3 results
shoots.216 = process_data(PS216, groupvars, measurevar, scale_treatment = "noninoculated")$shoots
roots.216 = process_data(PS216, groupvars, measurevar, scale_treatment = "noninoculated")$roots
shoots.218 = process_data(PS218, groupvars, measurevar, scale_treatment = "noninoculated")$shoots
roots.218 = process_data(PS218, groupvars, measurevar, scale_treatment = "noninoculated")$roots
y_scales1 = list(transcript == "PTI5" ~ scale_y_continuous(limits = c(0, 11), breaks = seq(0, 11, 1))
, transcript == "13-LOX" ~ scale_y_continuous(limits = c(0, 11), breaks = seq(0, 11, 1)))
y_scales2 = list(transcript == "RBOHD" ~ scale_y_continuous(limits = c(0, NA), breaks = seq(0, 11, 0.5))
, transcript == "PR1B" ~ scale_y_continuous(limits = c(0,NA), breaks = seq(0, 11, 1))
, transcript == "CPI8" ~ scale_y_continuous(limits = c(0, NA), breaks = seq(0, 11, 1))
, transcript == "CAB" ~ scale_y_continuous(limits = c(0, NA), breaks = seq(0, 11, 1))
, transcript == "BGLU2" ~ scale_y_continuous(limits = c(0, NA), breaks = seq(0, 11, 0.5))
, transcript == "HSP70" ~ scale_y_continuous(limits = c(0, NA), breaks = seq(0, 11, 0.5))
)
result = test_and_plot(data_long_raw = shoots.216
, myorder = myorder
, pal = pal
, what = "shoots 216"
, plot_gene_expression_func = plot_gene_expression
, groupvars = c("Treatment", "transcript")
, y_scales1
, y_scales2
)
## Shapiro-Wilk Normality Test
## # A tibble: 8 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 RBOHD 0.947 0.682 1
## 2 PR1B 0.601 0.000161 0.00129
## 3 CPI8 0.918 0.415 1
## 4 CAB 0.953 0.745 1
## 5 BGLU2 0.930 0.520 1
## 6 HSP70 0.962 0.829 1
## 7 PTI5 0.879 0.186 1
## 8 13-LOX 0.790 0.0226 0.158
## # A tibble: 8 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 RBOHD 0.947 0.682 1
## 2 PR1B 0.601 0.000161 0.00129
## 3 CPI8 0.918 0.415 1
## 4 CAB 0.953 0.745 1
## 5 BGLU2 0.930 0.520 1
## 6 HSP70 0.962 0.829 1
## 7 PTI5 0.879 0.186 1
## 8 13-LOX 0.790 0.0226 0.158
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 RBOHD 1 6 0.181 0.685
## 2 PR1B 1 6 0.478 0.515
## 3 CPI8 1 6 0.725 0.427
## 4 CAB 1 6 0.00927 0.926
## 5 BGLU2 1 6 0.739 0.423
## 6 HSP70 1 6 0.0138 0.910
## 7 PTI5 1 6 1.69 0.242
## 8 13-LOX 1 6 2.61 0.157
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 RBOHD 1 6 0.181 0.685
## 2 PR1B 1 6 0.478 0.515
## 3 CPI8 1 6 0.725 0.427
## 4 CAB 1 6 0.00927 0.926
## 5 BGLU2 1 6 0.739 0.423
## 6 HSP70 1 6 0.0138 0.910
## 7 PTI5 1 6 1.69 0.242
## 8 13-LOX 1 6 2.61 0.157
## Wilcoxon effect size
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.102 RBOHD 4 4 small
## 2 measurement noninoculated inoculated 0.306 PR1B 4 4 moderate
## 3 measurement noninoculated inoculated 0.510 CPI8 4 4 large
## 4 measurement noninoculated inoculated 0.102 CAB 4 4 small
## 5 measurement noninoculated inoculated 0.612 BGLU2 4 4 large
## 6 measurement noninoculated inoculated 0.204 HSP70 4 4 small
## 7 measurement noninoculated inoculated 0.816 PTI5 4 4 large
## 8 measurement noninoculated inoculated 0.714 13-LOX 4 4 large
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.102 RBOHD 4 4 small
## 2 scaled noninoculated inoculated 0.306 PR1B 4 4 moderate
## 3 scaled noninoculated inoculated 0.510 CPI8 4 4 large
## 4 scaled noninoculated inoculated 0.102 CAB 4 4 small
## 5 scaled noninoculated inoculated 0.612 BGLU2 4 4 large
## 6 scaled noninoculated inoculated 0.204 HSP70 4 4 small
## 7 scaled noninoculated inoculated 0.816 PTI5 4 4 large
## 8 scaled noninoculated inoculated 0.714 13-LOX 4 4 large
## Cohen's d Measure of Effect Size
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated -0.269 RBOHD 4 4 small
## 2 measurement noninoculated inoculated -0.696 PR1B 4 4 moderate
## 3 measurement noninoculated inoculated -1.21 CPI8 4 4 large
## 4 measurement noninoculated inoculated -0.425 CAB 4 4 small
## 5 measurement noninoculated inoculated -1.44 BGLU2 4 4 large
## 6 measurement noninoculated inoculated -0.389 HSP70 4 4 small
## 7 measurement noninoculated inoculated -4.45 PTI5 4 4 large
## 8 measurement noninoculated inoculated -1.68 13-LOX 4 4 large
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated -0.269 RBOHD 4 4 small
## 2 scaled noninoculated inoculated -0.696 PR1B 4 4 moderate
## 3 scaled noninoculated inoculated -1.21 CPI8 4 4 large
## 4 scaled noninoculated inoculated -0.425 CAB 4 4 small
## 5 scaled noninoculated inoculated -1.44 BGLU2 4 4 large
## 6 scaled noninoculated inoculated -0.389 HSP70 4 4 small
## 7 scaled noninoculated inoculated -4.45 PTI5 4 4 large
## 8 scaled noninoculated inoculated -1.68 13-LOX 4 4 large
res = result$stat.test[, grep("transcript|group1|group2|^p", colnames(result$stat.test))]
print(res)## transcript group1 group2 p p.adj p.adj.signif
## 1 RBOHD noninoculated inoculated 0.54285714 0.5857143 ns
## 2 PR1B noninoculated inoculated 0.40000000 0.5857143 ns
## 3 CPI8 noninoculated inoculated 0.18571429 0.3714286 ns
## 4 CAB noninoculated inoculated 0.58571429 0.5857143 ns
## 5 BGLU2 noninoculated inoculated 0.02857143 0.1142857 ns
## 6 HSP70 noninoculated inoculated 0.51428571 0.5857143 ns
## 7 PTI5 noninoculated inoculated 0.01428571 0.1142857 ns
## 8 13-LOX noninoculated inoculated 0.04285714 0.1142857 nsoutput_text = capture.output(paste0("Test - shoots - 216"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
output_text = capture.output(res)
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
ggsave(
filename = file.path(my_dir, "Test_shoots.216_1.pdf"),
plot = result$plot1,
device = pdf,
path = NULL,
scale = 1,
width = 3,
height = 8,
units = c("in"),
dpi = 900,
limitsize = TRUE,
bg = NULL
)
ggsave(
filename = file.path(my_dir, "Test_shoots.216_2.pdf"),
plot = result$plot2,
device = pdf,
path = NULL,
scale = 1,
width = 9,
height = 8,
units = c("in"),
dpi = 900,
limitsize = TRUE,
bg = NULL
)
result = test_and_plot(data_long_raw = roots.216
, myorder = myorder
, pal = pal
, what = "roots 216"
, plot_gene_expression_func = plot_gene_expression
, groupvars = c("Treatment", "transcript")
, y_scales1
, y_scales2
)
## Shapiro-Wilk Normality Test
## # A tibble: 8 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 RBOHD 0.921 0.438 1
## 2 PR1B 0.528 0.0000222 0.000177
## 3 CPI8 0.824 0.0513 0.257
## 4 CAB 0.786 0.0204 0.122
## 5 BGLU2 0.555 0.0000471 0.000330
## 6 HSP70 0.955 0.765 1
## 7 PTI5 0.828 0.0572 0.257
## 8 13-LOX 0.915 0.391 1
## # A tibble: 8 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 RBOHD 0.921 0.438 1
## 2 PR1B 0.528 0.0000222 0.000177
## 3 CPI8 0.824 0.0513 0.257
## 4 CAB 0.786 0.0204 0.122
## 5 BGLU2 0.555 0.0000471 0.000330
## 6 HSP70 0.955 0.765 1
## 7 PTI5 0.828 0.0572 0.257
## 8 13-LOX 0.915 0.391 1
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 RBOHD 1 6 1.19 0.317
## 2 PR1B 1 6 1.00 0.356
## 3 CPI8 1 6 7.39 0.0347
## 4 CAB 1 6 3.90 0.0956
## 5 BGLU2 1 6 0.792 0.408
## 6 HSP70 1 6 0.0123 0.915
## 7 PTI5 1 6 9.03 0.0238
## 8 13-LOX 1 6 0.258 0.630
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 RBOHD 1 6 1.19 0.317
## 2 PR1B 1 6 1.00 0.356
## 3 CPI8 1 6 7.39 0.0347
## 4 CAB 1 6 3.90 0.0956
## 5 BGLU2 1 6 0.792 0.408
## 6 HSP70 1 6 0.0123 0.915
## 7 PTI5 1 6 9.03 0.0238
## 8 13-LOX 1 6 0.258 0.630
## Wilcoxon effect size
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.306 RBOHD 4 4 moderate
## 2 measurement noninoculated inoculated 0.102 PR1B 4 4 small
## 3 measurement noninoculated inoculated 0.408 CPI8 4 4 moderate
## 4 measurement noninoculated inoculated 0.408 CAB 4 4 moderate
## 5 measurement noninoculated inoculated 0.306 BGLU2 4 4 moderate
## 6 measurement noninoculated inoculated 0.816 HSP70 4 4 large
## 7 measurement noninoculated inoculated 0.816 PTI5 4 4 large
## 8 measurement noninoculated inoculated 0.816 13-LOX 4 4 large
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.306 RBOHD 4 4 moderate
## 2 scaled noninoculated inoculated 0.102 PR1B 4 4 small
## 3 scaled noninoculated inoculated 0.408 CPI8 4 4 moderate
## 4 scaled noninoculated inoculated 0.408 CAB 4 4 moderate
## 5 scaled noninoculated inoculated 0.306 BGLU2 4 4 moderate
## 6 scaled noninoculated inoculated 0.816 HSP70 4 4 large
## 7 scaled noninoculated inoculated 0.816 PTI5 4 4 large
## 8 scaled noninoculated inoculated 0.816 13-LOX 4 4 large
## Cohen's d Measure of Effect Size
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.318 RBOHD 4 4 small
## 2 measurement noninoculated inoculated -0.653 PR1B 4 4 moderate
## 3 measurement noninoculated inoculated -1.26 CPI8 4 4 large
## 4 measurement noninoculated inoculated -1.60 CAB 4 4 large
## 5 measurement noninoculated inoculated -0.499 BGLU2 4 4 small
## 6 measurement noninoculated inoculated -2.82 HSP70 4 4 large
## 7 measurement noninoculated inoculated -2.43 PTI5 4 4 large
## 8 measurement noninoculated inoculated -3.09 13-LOX 4 4 large
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.318 RBOHD 4 4 small
## 2 scaled noninoculated inoculated -0.653 PR1B 4 4 moderate
## 3 scaled noninoculated inoculated -1.26 CPI8 4 4 large
## 4 scaled noninoculated inoculated -1.60 CAB 4 4 large
## 5 scaled noninoculated inoculated -0.499 BGLU2 4 4 small
## 6 scaled noninoculated inoculated -2.82 HSP70 4 4 large
## 7 scaled noninoculated inoculated -2.43 PTI5 4 4 large
## 8 scaled noninoculated inoculated -3.09 13-LOX 4 4 large
res = result$stat.test[, grep("transcript|group1|group2|^p", colnames(result$stat.test))]
print(res)## transcript group1 group2 p p.adj p.adj.signif
## 1 RBOHD noninoculated inoculated 0.54285714 0.62040816 ns
## 2 PR1B noninoculated inoculated 0.47142857 0.62040816 ns
## 3 CPI8 noninoculated inoculated 0.14285714 0.22857143 ns
## 4 CAB noninoculated inoculated 0.10000000 0.20000000 ns
## 5 BGLU2 noninoculated inoculated 0.94285714 0.94285714 ns
## 6 HSP70 noninoculated inoculated 0.01428571 0.03809524 *
## 7 PTI5 noninoculated inoculated 0.01428571 0.03809524 *
## 8 13-LOX noninoculated inoculated 0.01428571 0.03809524 *output_text = capture.output(paste0("Test - roots - 216"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
output_text = capture.output(res)
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
ggsave(
filename = file.path(my_dir, "Test_roots.216_1.pdf"),
plot = result$plot1,
device = pdf,
path = NULL,
scale = 1,
width = 3,
height = 8,
units = c("in"),
dpi = 900,
limitsize = TRUE,
bg = NULL
)
ggsave(
filename = file.path(my_dir, "Test_roots.216_2.pdf"),
plot = result$plot2,
device = pdf,
path = NULL,
scale = 1,
width = 9,
height = 8,
units = c("in"),
dpi = 900,
limitsize = TRUE,
bg = NULL
)
result = test_and_plot(data_long_raw = shoots.218
, myorder = myorder
, pal = pal
, what = "shoots 218"
, plot_gene_expression_func = plot_gene_expression
, groupvars = c("Treatment", "transcript")
, y_scales1
, y_scales2
)
## Shapiro-Wilk Normality Test
## # A tibble: 8 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 RBOHD 0.943 0.644 1
## 2 PR1B 0.561 0.0000546 0.000437
## 3 CPI8 0.744 0.00700 0.0490
## 4 CAB 0.945 0.658 1
## 5 BGLU2 0.906 0.326 1
## 6 HSP70 0.939 0.598 1
## 7 PTI5 0.906 0.326 1
## 8 13-LOX 0.898 0.279 1
## # A tibble: 8 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 RBOHD 0.943 0.644 1
## 2 PR1B 0.561 0.0000546 0.000437
## 3 CPI8 0.744 0.00700 0.0490
## 4 CAB 0.945 0.658 1
## 5 BGLU2 0.906 0.326 1
## 6 HSP70 0.939 0.598 1
## 7 PTI5 0.906 0.326 1
## 8 13-LOX 0.898 0.279 1
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 RBOHD 1 6 0.255 0.631
## 2 PR1B 1 6 0.585 0.473
## 3 CPI8 1 6 3.74 0.101
## 4 CAB 1 6 0.0364 0.855
## 5 BGLU2 1 6 0.00507 0.946
## 6 HSP70 1 6 0.000737 0.979
## 7 PTI5 1 6 0.0321 0.864
## 8 13-LOX 1 6 1.91 0.216
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 RBOHD 1 6 0.255 0.631
## 2 PR1B 1 6 0.585 0.473
## 3 CPI8 1 6 3.74 0.101
## 4 CAB 1 6 0.0364 0.855
## 5 BGLU2 1 6 0.00507 0.946
## 6 HSP70 1 6 0.000737 0.979
## 7 PTI5 1 6 0.0321 0.864
## 8 13-LOX 1 6 1.91 0.216
## Wilcoxon effect size
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.408 RBOHD 4 4 moderate
## 2 measurement noninoculated inoculated 0.204 PR1B 4 4 small
## 3 measurement noninoculated inoculated 0 CPI8 4 4 small
## 4 measurement noninoculated inoculated 0.306 CAB 4 4 moderate
## 5 measurement noninoculated inoculated 0.612 BGLU2 4 4 large
## 6 measurement noninoculated inoculated 0.612 HSP70 4 4 large
## 7 measurement noninoculated inoculated 0.816 PTI5 4 4 large
## 8 measurement noninoculated inoculated 0.612 13-LOX 4 4 large
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.408 RBOHD 4 4 moderate
## 2 scaled noninoculated inoculated 0.204 PR1B 4 4 small
## 3 scaled noninoculated inoculated 0 CPI8 4 4 small
## 4 scaled noninoculated inoculated 0.306 CAB 4 4 moderate
## 5 scaled noninoculated inoculated 0.612 BGLU2 4 4 large
## 6 scaled noninoculated inoculated 0.612 HSP70 4 4 large
## 7 scaled noninoculated inoculated 0.816 PTI5 4 4 large
## 8 scaled noninoculated inoculated 0.612 13-LOX 4 4 large
## Cohen's d Measure of Effect Size
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated -1.23 RBOHD 4 4 large
## 2 measurement noninoculated inoculated -0.702 PR1B 4 4 moderate
## 3 measurement noninoculated inoculated 0.436 CPI8 4 4 small
## 4 measurement noninoculated inoculated -0.615 CAB 4 4 moderate
## 5 measurement noninoculated inoculated -1.79 BGLU2 4 4 large
## 6 measurement noninoculated inoculated -1.77 HSP70 4 4 large
## 7 measurement noninoculated inoculated -4.62 PTI5 4 4 large
## 8 measurement noninoculated inoculated -1.37 13-LOX 4 4 large
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated -1.23 RBOHD 4 4 large
## 2 scaled noninoculated inoculated -0.702 PR1B 4 4 moderate
## 3 scaled noninoculated inoculated 0.436 CPI8 4 4 small
## 4 scaled noninoculated inoculated -0.615 CAB 4 4 moderate
## 5 scaled noninoculated inoculated -1.79 BGLU2 4 4 large
## 6 scaled noninoculated inoculated -1.77 HSP70 4 4 large
## 7 scaled noninoculated inoculated -4.62 PTI5 4 4 large
## 8 scaled noninoculated inoculated -1.37 13-LOX 4 4 large
res = result$stat.test[, grep("transcript|group1|group2|^p", colnames(result$stat.test))]
print(res)## transcript group1 group2 p p.adj p.adj.signif
## 1 RBOHD noninoculated inoculated 0.11428571 0.1828571 ns
## 2 PR1B noninoculated inoculated 0.37142857 0.4952381 ns
## 3 CPI8 noninoculated inoculated 0.70000000 0.7000000 ns
## 4 CAB noninoculated inoculated 0.48571429 0.5551020 ns
## 5 BGLU2 noninoculated inoculated 0.05714286 0.1828571 ns
## 6 HSP70 noninoculated inoculated 0.11428571 0.1828571 ns
## 7 PTI5 noninoculated inoculated 0.08571429 0.1828571 ns
## 8 13-LOX noninoculated inoculated 0.05714286 0.1828571 nsoutput_text = capture.output(paste0("Test - shoots - 218"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
output_text = capture.output(res)
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
ggsave(
filename = file.path(my_dir, "Test_shoots.218_1.pdf"),
plot = result$plot1,
device = pdf,
path = NULL,
scale = 1,
width = 3,
height = 8,
units = c("in"),
dpi = 900,
limitsize = TRUE,
bg = NULL
)
ggsave(
filename = file.path(my_dir, "Test_shoots.218_2.pdf"),
plot = result$plot2,
device = pdf,
path = NULL,
scale = 1,
width = 9,
height = 8,
units = c("in"),
dpi = 900,
limitsize = TRUE,
bg = NULL
)
result = test_and_plot(data_long_raw = roots.218
, myorder = myorder
, pal = pal
, what = "roots 218"
, plot_gene_expression_func = plot_gene_expression
, groupvars = c("Treatment", "transcript")
, y_scales1
, y_scales2
)
## Shapiro-Wilk Normality Test
## # A tibble: 8 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 RBOHD 0.875 0.170 0.678
## 2 PR1B 0.637 0.000424 0.00339
## 3 CPI8 0.802 0.0300 0.180
## 4 CAB 0.777 0.0162 0.114
## 5 BGLU2 0.894 0.254 0.763
## 6 HSP70 0.899 0.280 0.763
## 7 PTI5 0.826 0.0534 0.267
## 8 13-LOX 0.906 0.327 0.763
## # A tibble: 8 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 RBOHD 0.875 0.170 0.678
## 2 PR1B 0.637 0.000424 0.00339
## 3 CPI8 0.802 0.0300 0.180
## 4 CAB 0.777 0.0162 0.114
## 5 BGLU2 0.894 0.254 0.763
## 6 HSP70 0.899 0.280 0.763
## 7 PTI5 0.826 0.0534 0.267
## 8 13-LOX 0.906 0.327 0.763
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 RBOHD 1 6 8.01 0.0300
## 2 PR1B 1 6 0.668 0.445
## 3 CPI8 1 6 2.55 0.161
## 4 CAB 1 6 6.52 0.0433
## 5 BGLU2 1 6 1.96 0.211
## 6 HSP70 1 6 0.0221 0.887
## 7 PTI5 1 6 24.4 0.00260
## 8 13-LOX 1 6 4.36 0.0818
## # A tibble: 8 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 RBOHD 1 6 8.01 0.0300
## 2 PR1B 1 6 0.668 0.445
## 3 CPI8 1 6 2.55 0.161
## 4 CAB 1 6 6.52 0.0433
## 5 BGLU2 1 6 1.96 0.211
## 6 HSP70 1 6 0.0221 0.887
## 7 PTI5 1 6 24.4 0.00260
## 8 13-LOX 1 6 4.36 0.0818
## Wilcoxon effect size
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.306 RBOHD 4 4 moderate
## 2 measurement noninoculated inoculated 0.408 PR1B 4 4 moderate
## 3 measurement noninoculated inoculated 0.510 CPI8 4 4 large
## 4 measurement noninoculated inoculated 0.816 CAB 4 4 large
## 5 measurement noninoculated inoculated 0.408 BGLU2 4 4 moderate
## 6 measurement noninoculated inoculated 0.204 HSP70 4 4 small
## 7 measurement noninoculated inoculated 0.816 PTI5 4 4 large
## 8 measurement noninoculated inoculated 0.816 13-LOX 4 4 large
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.306 RBOHD 4 4 moderate
## 2 scaled noninoculated inoculated 0.408 PR1B 4 4 moderate
## 3 scaled noninoculated inoculated 0.510 CPI8 4 4 large
## 4 scaled noninoculated inoculated 0.816 CAB 4 4 large
## 5 scaled noninoculated inoculated 0.408 BGLU2 4 4 moderate
## 6 scaled noninoculated inoculated 0.204 HSP70 4 4 small
## 7 scaled noninoculated inoculated 0.816 PTI5 4 4 large
## 8 scaled noninoculated inoculated 0.816 13-LOX 4 4 large
## Cohen's d Measure of Effect Size
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated -0.928 RBOHD 4 4 large
## 2 measurement noninoculated inoculated -0.769 PR1B 4 4 moderate
## 3 measurement noninoculated inoculated -1.44 CPI8 4 4 large
## 4 measurement noninoculated inoculated -1.94 CAB 4 4 large
## 5 measurement noninoculated inoculated 0.906 BGLU2 4 4 large
## 6 measurement noninoculated inoculated -0.560 HSP70 4 4 moderate
## 7 measurement noninoculated inoculated -2.12 PTI5 4 4 large
## 8 measurement noninoculated inoculated -2.81 13-LOX 4 4 large
## # A tibble: 8 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated -0.928 RBOHD 4 4 large
## 2 scaled noninoculated inoculated -0.769 PR1B 4 4 moderate
## 3 scaled noninoculated inoculated -1.44 CPI8 4 4 large
## 4 scaled noninoculated inoculated -1.94 CAB 4 4 large
## 5 scaled noninoculated inoculated 0.906 BGLU2 4 4 large
## 6 scaled noninoculated inoculated -0.560 HSP70 4 4 moderate
## 7 scaled noninoculated inoculated -2.12 PTI5 4 4 large
## 8 scaled noninoculated inoculated -2.81 13-LOX 4 4 large
res = result$stat.test[, grep("transcript|group1|group2|^p", colnames(result$stat.test))]
print(res)## transcript group1 group2 p p.adj p.adj.signif
## 1 RBOHD noninoculated inoculated 0.20000000 0.26666667 ns
## 2 PR1B noninoculated inoculated 0.28571429 0.32653061 ns
## 3 CPI8 noninoculated inoculated 0.12857143 0.25714286 ns
## 4 CAB noninoculated inoculated 0.01428571 0.03809524 *
## 5 BGLU2 noninoculated inoculated 0.20000000 0.26666667 ns
## 6 HSP70 noninoculated inoculated 0.60000000 0.60000000 ns
## 7 PTI5 noninoculated inoculated 0.01428571 0.03809524 *
## 8 13-LOX noninoculated inoculated 0.01428571 0.03809524 *output_text = capture.output(paste0("Test - roots - 218"))
cat(output_text, file = fr, append = TRUE, sep = "\n")
output_text = capture.output(res)
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
ggsave(
filename = file.path(my_dir, "Test_roots.218_1.pdf"),
plot = result$plot1,
device = pdf,
path = NULL,
scale = 1,
width = 3,
height = 8,
units = c("in"),
dpi = 900,
limitsize = TRUE,
bg = NULL
)
ggsave(
filename = file.path(my_dir, "Test_roots.218_2.pdf"),
plot = result$plot2,
device = pdf,
path = NULL,
scale = 1,
width = 9,
height = 8,
units = c("in"),
dpi = 900,
limitsize = TRUE,
bg = NULL
)5 Exp1
5.1 input
## [1] "README.md" "Table S1.xlsx" "Table S2.xlsx" "Table S7.xlsx"fn = 'Table S7.xlsx'
PS218 = openxlsx::read.xlsx(xlsxFile = file.path(fp, fn),
sheet = 'Exp1',
startRow = 1,
colNames = TRUE,
rowNames = FALSE,
detectDates = FALSE,
skipEmptyRows = TRUE,
skipEmptyCols = TRUE,
rows = NULL,
cols = NULL,
check.names = FALSE,
sep.names = ".",
namedRegion = NULL,
na.strings = "NA",
fillMergedCells = FALSE)
data.table::setDT(PS218)
PS218[, Sample.ID := NULL]
PS218[, Genotype := NULL]
PS218[, Strain := NULL]
PS218$Tissue = as.factor(trimws(PS218$Tissue))
PS218$Treatment = factor(trimws(PS218$Treatment), levels = c("noninoculated", "inoculated"))
table(PS218$Time)##
## 2 h 4 h 6 h
## 16 16 165.2 params
5.3 results
run_analysis_for_time <- function(data
, time_point
, myorder
, pal
, groupvars
, measurevar
, y_scales1
, y_scales2
, y_scales3
, y_scales4
, my_dir
, plot_gene_expression
, test_and_plot) {
temp = data[data$Time == time_point, ]
temp[, Time := NULL]
shoots = process_data(temp, groupvars, measurevar, scale_treatment = "noninoculated")$shoots
roots = process_data(temp, groupvars, measurevar, scale_treatment = "noninoculated")$roots
result_shoots = test_and_plot(data_long_raw = shoots,
myorder = myorder,
pal = pal,
what = paste("shoots", time_point),
plot_gene_expression_func = plot_gene_expression,
groupvars = groupvars,
y_scales1 = y_scales1,
y_scales2 = y_scales2)
res = result_shoots$stat.test[, grep("transcript|group1|group2|^p", colnames(result_shoots$stat.test))]
print(res)
output_text = capture.output(paste0("Exp 1 shoots - ", time_point))
cat(output_text, file = fr, append = TRUE, sep = "\n")
output_text = capture.output(res)
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
ggsave(filename = file.path(my_dir, paste0("Exp1_shoots.", gsub(" ", "", time_point), "_1.pdf")),
plot = result_shoots$plot1, device = pdf, width = 3, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Exp1_shoots.", gsub(" ", "", time_point), "_2.pdf")),
plot = result_shoots$plot2, device = pdf, width = 3, height = 8, units = "in", dpi = 900)
result_roots = test_and_plot(data_long_raw = roots,
myorder = myorder,
pal = pal,
what = paste("roots", time_point),
plot_gene_expression_func = plot_gene_expression,
groupvars = groupvars,
y_scales1 = y_scales3,
y_scales2 = y_scales4)
res = result_roots$stat.test[, grep("transcript|group1|group2|^p", colnames(result_roots$stat.test))]
print(res)
output_text = capture.output(paste0("Exp 1 roots - ", time_point))
cat(output_text, file = fr, append = TRUE, sep = "\n")
output_text = capture.output(res)
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
ggsave(filename = file.path(my_dir, paste0("Exp1_roots.", gsub(" ", ".", time_point), "_1.pdf")),
plot = result_roots$plot1, device = pdf, width = 3, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Exp1_roots.", gsub(" ", ".", time_point), "_2.pdf")),
plot = result_roots$plot2, device = pdf, width = 3, height = 8, units = "in", dpi = 900)
list(shoots = result_shoots, roots = result_roots)
}
y_scales1 = list(transcript == "PTI5" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 1))
, transcript == "13-LOX" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 1)))
y_scales2 = list(transcript == "BGLU2" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 0.5))
, transcript == "HSP70" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 0.5))
)
y_scales3 = list(transcript == "PTI5" ~ scale_y_continuous(limits = c(0, 11), breaks = seq(0, 11, 1))
, transcript == "13-LOX" ~ scale_y_continuous(limits = c(0, 11), breaks = seq(0, 11, 1)))
y_scales4 = list(transcript == "BGLU2" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 0.5))
, transcript == "HSP70" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 0.5))
)
results_2h = run_analysis_for_time(data = PS218
, time_point = "2 h"
, myorder
, pal
, groupvars
, measurevar
, y_scales1
, y_scales2
, y_scales3
, y_scales4
, my_dir
, plot_gene_expression
, test_and_plot)
## Shapiro-Wilk Normality Test
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.907 0.335 1
## 2 HSP70 0.954 0.749 1
## 3 PTI5 0.931 0.524 1
## 4 13-LOX 0.956 0.773 1
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.907 0.335 1
## 2 HSP70 0.954 0.749 1
## 3 PTI5 0.931 0.524 1
## 4 13-LOX 0.956 0.773 1
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 1.91 0.216
## 2 HSP70 1 6 0.0663 0.805
## 3 PTI5 1 6 0.156 0.707
## 4 13-LOX 1 6 0.0281 0.872
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 1.91 0.216
## 2 HSP70 1 6 0.0663 0.805
## 3 PTI5 1 6 0.156 0.707
## 4 13-LOX 1 6 0.0281 0.872
## Wilcoxon effect size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.510 BGLU2 4 4 large
## 2 measurement noninoculated inoculated 0.102 HSP70 4 4 small
## 3 measurement noninoculated inoculated 0.612 PTI5 4 4 large
## 4 measurement noninoculated inoculated 0 13-LOX 4 4 small
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.510 BGLU2 4 4 large
## 2 scaled noninoculated inoculated 0.102 HSP70 4 4 small
## 3 scaled noninoculated inoculated 0.612 PTI5 4 4 large
## 4 scaled noninoculated inoculated 0 13-LOX 4 4 small
## Cohen's d Measure of Effect Size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 1.13 BGLU2 4 4 large
## 2 measurement noninoculated inoculated -0.176 HSP70 4 4 negligible
## 3 measurement noninoculated inoculated -1.19 PTI5 4 4 large
## 4 measurement noninoculated inoculated 0.0435 13-LOX 4 4 negligible
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 1.13 BGLU2 4 4 large
## 2 scaled noninoculated inoculated -0.176 HSP70 4 4 negligible
## 3 scaled noninoculated inoculated -1.19 PTI5 4 4 large
## 4 scaled noninoculated inoculated 0.0435 13-LOX 4 4 negligible
## transcript group1 group2 p p.adj p.adj.signif
## 1 BGLU2 noninoculated inoculated 0.1000000 0.2285714 ns
## 2 HSP70 noninoculated inoculated 0.8428571 0.9571429 ns
## 3 PTI5 noninoculated inoculated 0.1142857 0.2285714 ns
## 4 13-LOX noninoculated inoculated 0.9571429 0.9571429 ns
## Shapiro-Wilk Normality Test
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.886 0.252 0.252
## 2 HSP70 0.787 0.0209 0.0626
## 3 PTI5 0.814 0.0404 0.0808
## 4 13-LOX 0.761 0.0108 0.0434
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.886 0.252 0.252
## 2 HSP70 0.787 0.0209 0.0626
## 3 PTI5 0.814 0.0404 0.0808
## 4 13-LOX 0.761 0.0108 0.0434
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 5 2.63 0.166
## 2 HSP70 1 6 38.9 0.000785
## 3 PTI5 1 6 4.06 0.0906
## 4 13-LOX 1 6 0.370 0.566
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 5 2.63 0.166
## 2 HSP70 1 6 38.9 0.000785
## 3 PTI5 1 6 4.06 0.0906
## 4 13-LOX 1 6 0.370 0.566
## Wilcoxon effect size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.267 BGLU2 4 3 small
## 2 measurement noninoculated inoculated 0.204 HSP70 4 4 small
## 3 measurement noninoculated inoculated 0.816 PTI5 4 4 large
## 4 measurement noninoculated inoculated 0.816 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.267 BGLU2 4 3 small
## 2 scaled noninoculated inoculated 0.204 HSP70 4 4 small
## 3 scaled noninoculated inoculated 0.816 PTI5 4 4 large
## 4 scaled noninoculated inoculated 0.816 13-LOX 4 4 large
## Cohen's d Measure of Effect Size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated -0.789 BGLU2 4 3 moderate
## 2 measurement noninoculated inoculated -0.823 HSP70 4 4 large
## 3 measurement noninoculated inoculated -1.99 PTI5 4 4 large
## 4 measurement noninoculated inoculated -11.7 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated -0.789 BGLU2 4 3 moderate
## 2 scaled noninoculated inoculated -0.823 HSP70 4 4 large
## 3 scaled noninoculated inoculated -1.99 PTI5 4 4 large
## 4 scaled noninoculated inoculated -11.7 13-LOX 4 4 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 BGLU2 noninoculated inoculated 0.40000000 0.40000000 ns
## 2 HSP70 noninoculated inoculated 0.35714286 0.40000000 ns
## 3 PTI5 noninoculated inoculated 0.01428571 0.02857143 *
## 4 13-LOX noninoculated inoculated 0.01428571 0.02857143 *
y_scales1 = list(transcript == "PTI5" ~ scale_y_continuous(limits = c(0, 5), breaks = seq(0, 11, 1))
, transcript == "13-LOX" ~ scale_y_continuous(limits = c(0, 5), breaks = seq(0, 11, 1)))
results_4h = run_analysis_for_time(data = PS218
, time_point = "4 h"
, myorder
, pal
, groupvars
, measurevar
, y_scales1
, y_scales2
, y_scales3
, y_scales4
, my_dir
, plot_gene_expression
, test_and_plot)
## Shapiro-Wilk Normality Test
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.908 0.338 1
## 2 HSP70 0.914 0.387 1
## 3 PTI5 0.740 0.00989 0.0396
## 4 13-LOX 0.919 0.424 1
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.908 0.338 1
## 2 HSP70 0.914 0.387 1
## 3 PTI5 0.740 0.00989 0.0396
## 4 13-LOX 0.919 0.424 1
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 7.26 0.0358
## 2 HSP70 1 6 2.06 0.201
## 3 PTI5 1 5 3.62 0.115
## 4 13-LOX 1 6 0.0744 0.794
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 7.26 0.0358
## 2 HSP70 1 6 2.06 0.201
## 3 PTI5 1 5 3.62 0.115
## 4 13-LOX 1 6 0.0744 0.794
## Wilcoxon effect size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.408 BGLU2 4 4 moderate
## 2 measurement noninoculated inoculated 0.204 HSP70 4 4 small
## 3 measurement noninoculated inoculated 0.668 PTI5 4 3 large
## 4 measurement noninoculated inoculated 0.510 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.408 BGLU2 4 4 moderate
## 2 scaled noninoculated inoculated 0.204 HSP70 4 4 small
## 3 scaled noninoculated inoculated 0.668 PTI5 4 3 large
## 4 scaled noninoculated inoculated 0.510 13-LOX 4 4 large
## Cohen's d Measure of Effect Size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 1.14 BGLU2 4 4 large
## 2 measurement noninoculated inoculated -0.0196 HSP70 4 4 negligible
## 3 measurement noninoculated inoculated -1.41 PTI5 4 3 large
## 4 measurement noninoculated inoculated -0.802 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 1.14 BGLU2 4 4 large
## 2 scaled noninoculated inoculated -0.0196 HSP70 4 4 negligible
## 3 scaled noninoculated inoculated -1.41 PTI5 4 3 large
## 4 scaled noninoculated inoculated -0.802 13-LOX 4 4 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 BGLU2 noninoculated inoculated 0.20000000 0.4000000 ns
## 2 HSP70 noninoculated inoculated 0.97142857 0.9714286 ns
## 3 PTI5 noninoculated inoculated 0.04285714 0.1714286 ns
## 4 13-LOX noninoculated inoculated 0.37142857 0.4952381 ns
## Shapiro-Wilk Normality Test
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.908 0.343 0.832
## 2 HSP70 0.904 0.315 0.832
## 3 PTI5 0.781 0.0263 0.105
## 4 13-LOX 0.898 0.277 0.832
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.908 0.343 0.832
## 2 HSP70 0.904 0.315 0.832
## 3 PTI5 0.781 0.0263 0.105
## 4 13-LOX 0.898 0.277 0.832
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 0.0398 0.849
## 2 HSP70 1 6 0.494 0.508
## 3 PTI5 1 5 13.7 0.0140
## 4 13-LOX 1 6 2.52 0.164
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 0.0398 0.849
## 2 HSP70 1 6 0.494 0.508
## 3 PTI5 1 5 13.7 0.0140
## 4 13-LOX 1 6 2.52 0.164
## Wilcoxon effect size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.204 BGLU2 4 4 small
## 2 measurement noninoculated inoculated 0.714 HSP70 4 4 large
## 3 measurement noninoculated inoculated 0.802 PTI5 3 4 large
## 4 measurement noninoculated inoculated 0.816 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.204 BGLU2 4 4 small
## 2 scaled noninoculated inoculated 0.714 HSP70 4 4 large
## 3 scaled noninoculated inoculated 0.802 PTI5 3 4 large
## 4 scaled noninoculated inoculated 0.816 13-LOX 4 4 large
## Cohen's d Measure of Effect Size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.101 BGLU2 4 4 negligible
## 2 measurement noninoculated inoculated -2.66 HSP70 4 4 large
## 3 measurement noninoculated inoculated -1.62 PTI5 3 4 large
## 4 measurement noninoculated inoculated -2.65 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.101 BGLU2 4 4 negligible
## 2 scaled noninoculated inoculated -2.66 HSP70 4 4 large
## 3 scaled noninoculated inoculated -1.62 PTI5 3 4 large
## 4 scaled noninoculated inoculated -2.65 13-LOX 4 4 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 BGLU2 noninoculated inoculated 0.77142857 0.77142857 ns
## 2 HSP70 noninoculated inoculated 0.01428571 0.02857143 *
## 3 PTI5 noninoculated inoculated 0.04285714 0.05714286 ns
## 4 13-LOX noninoculated inoculated 0.01428571 0.02857143 *
results_6h = run_analysis_for_time(data = PS218
, time_point = "6 h"
, myorder
, pal
, groupvars
, measurevar
, y_scales1
, y_scales2
, y_scales3
, y_scales4
, my_dir
, plot_gene_expression
, test_and_plot)
## Shapiro-Wilk Normality Test
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.853 0.103 0.410
## 2 HSP70 0.975 0.932 1
## 3 PTI5 0.891 0.239 0.718
## 4 13-LOX 0.958 0.793 1
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.853 0.103 0.410
## 2 HSP70 0.975 0.932 1
## 3 PTI5 0.891 0.239 0.718
## 4 13-LOX 0.958 0.793 1
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 9.33 0.0224
## 2 HSP70 1 6 1.76 0.233
## 3 PTI5 1 6 0.632 0.457
## 4 13-LOX 1 6 0.157 0.705
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 9.33 0.0224
## 2 HSP70 1 6 1.76 0.233
## 3 PTI5 1 6 0.632 0.457
## 4 13-LOX 1 6 0.157 0.705
## Wilcoxon effect size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.204 BGLU2 4 4 small
## 2 measurement noninoculated inoculated 0.204 HSP70 4 4 small
## 3 measurement noninoculated inoculated 0.714 PTI5 4 4 large
## 4 measurement noninoculated inoculated 0.510 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.204 BGLU2 4 4 small
## 2 scaled noninoculated inoculated 0.204 HSP70 4 4 small
## 3 scaled noninoculated inoculated 0.714 PTI5 4 4 large
## 4 scaled noninoculated inoculated 0.510 13-LOX 4 4 large
## Cohen's d Measure of Effect Size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated -0.783 BGLU2 4 4 moderate
## 2 measurement noninoculated inoculated -0.167 HSP70 4 4 negligible
## 3 measurement noninoculated inoculated -2.26 PTI5 4 4 large
## 4 measurement noninoculated inoculated -1.01 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated -0.783 BGLU2 4 4 moderate
## 2 scaled noninoculated inoculated -0.167 HSP70 4 4 negligible
## 3 scaled noninoculated inoculated -2.26 PTI5 4 4 large
## 4 scaled noninoculated inoculated -1.01 13-LOX 4 4 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 BGLU2 noninoculated inoculated 0.41428571 0.5523810 ns
## 2 HSP70 noninoculated inoculated 0.84285714 0.8428571 ns
## 3 PTI5 noninoculated inoculated 0.04285714 0.1714286 ns
## 4 13-LOX noninoculated inoculated 0.14285714 0.2857143 ns
## Shapiro-Wilk Normality Test
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.854 0.105 0.315
## 2 HSP70 0.930 0.514 0.514
## 3 PTI5 0.839 0.0740 0.296
## 4 13-LOX 0.861 0.122 0.315
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.854 0.105 0.315
## 2 HSP70 0.930 0.514 0.514
## 3 PTI5 0.839 0.0740 0.296
## 4 13-LOX 0.861 0.122 0.315
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 1.48 0.269
## 2 HSP70 1 6 0.0375 0.853
## 3 PTI5 1 6 1.80 0.228
## 4 13-LOX 1 6 6.91 0.0391
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 1.48 0.269
## 2 HSP70 1 6 0.0375 0.853
## 3 PTI5 1 6 1.80 0.228
## 4 13-LOX 1 6 6.91 0.0391
## Wilcoxon effect size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.714 BGLU2 4 4 large
## 2 measurement noninoculated inoculated 0.306 HSP70 4 4 moderate
## 3 measurement noninoculated inoculated 0.816 PTI5 4 4 large
## 4 measurement noninoculated inoculated 0.816 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.714 BGLU2 4 4 large
## 2 scaled noninoculated inoculated 0.306 HSP70 4 4 moderate
## 3 scaled noninoculated inoculated 0.816 PTI5 4 4 large
## 4 scaled noninoculated inoculated 0.816 13-LOX 4 4 large
## Cohen's d Measure of Effect Size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated -2.10 BGLU2 4 4 large
## 2 measurement noninoculated inoculated -0.886 HSP70 4 4 large
## 3 measurement noninoculated inoculated -4.39 PTI5 4 4 large
## 4 measurement noninoculated inoculated -2.64 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated -2.10 BGLU2 4 4 large
## 2 scaled noninoculated inoculated -0.886 HSP70 4 4 large
## 3 scaled noninoculated inoculated -4.39 PTI5 4 4 large
## 4 scaled noninoculated inoculated -2.64 13-LOX 4 4 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 BGLU2 noninoculated inoculated 0.01428571 0.01904762 *
## 2 HSP70 noninoculated inoculated 0.27142857 0.27142857 ns
## 3 PTI5 noninoculated inoculated 0.01428571 0.01904762 *
## 4 13-LOX noninoculated inoculated 0.01428571 0.01904762 *
6 Exp2
6.1 input
## [1] "README.md" "Table S1.xlsx" "Table S2.xlsx" "Table S7.xlsx"fn = 'Table S7.xlsx'
PS218 = openxlsx::read.xlsx(xlsxFile = file.path(fp, fn),
sheet = 'Exp2',
startRow = 1,
colNames = TRUE,
rowNames = FALSE,
detectDates = FALSE,
skipEmptyRows = TRUE,
skipEmptyCols = TRUE,
rows = NULL,
cols = NULL,
check.names = FALSE,
sep.names = ".",
namedRegion = NULL,
na.strings = "NA",
fillMergedCells = FALSE)
data.table::setDT(PS218)
PS218[, Sample.ID := NULL]
PS218[, Genotype := NULL]
PS218[, Strain := NULL]
PS218$Tissue = as.factor(trimws(PS218$Tissue))
PS218$Treatment = factor(trimws(PS218$Treatment), levels = c("noninoculated", "inoculated"))
table(PS218$Time)##
## 1 h 1 min 15 min 2 h 30 min
## 8 8 8 8 86.2 params
6.3 results
run_analysis_for_time <- function(data
, time_point
, myorder
, pal
, groupvars
, measurevar
, y_scales1
, y_scales2
, my_dir
, plot_gene_expression
, test_and_plot
) {
temp = data[data$Time == time_point, ]
temp[, Time := NULL]
roots = process_data(temp, groupvars, measurevar, scale_treatment = "noninoculated")$roots
result_roots = test_and_plot(data_long_raw = roots,
myorder = myorder,
pal = pal,
what = paste("roots", time_point),
plot_gene_expression_func = plot_gene_expression,
groupvars = groupvars,
y_scales1,
y_scales2)
res = result_roots$stat.test[, grep("transcript|group1|group2|^p", colnames(result_roots$stat.test))]
print(res)
output_text = capture.output(paste0("Exp 2 roots - ", time_point))
cat(output_text, file = fr, append = TRUE, sep = "\n")
output_text = capture.output(res)
cat(output_text, file = fr, append = TRUE, sep = "\n")
cat("", file = fr, append = TRUE, sep = "\n")
ggsave(filename = file.path(my_dir, paste0("Exp2_roots.", gsub(" ", ".", time_point), "_1.pdf")),
plot = result_roots$plot1, device = pdf, width = 3, height = 8, units = "in", dpi = 900)
ggsave(filename = file.path(my_dir, paste0("Exp2_roots.", gsub(" ", ".", time_point), "_2.pdf")),
plot = result_roots$plot2, device = pdf, width = 3, height = 8, units = "in", dpi = 900)
list(roots = result_roots)
}
y_scales1 = list(transcript == "PTI5" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 1))
, transcript == "13-LOX" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 1)))
y_scales2 = list(transcript == "BGLU2" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 0.5))
, transcript == "HSP70" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 0.5))
)
results_1min = run_analysis_for_time(data = PS218
, time_point = "1 min"
, myorder
, pal
, groupvars
, measurevar
, y_scales1
, y_scales2
, my_dir
, plot_gene_expression
, test_and_plot
)
## Shapiro-Wilk Normality Test
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.898 0.278 0.835
## 2 HSP70 0.956 0.774 0.863
## 3 PTI5 0.920 0.431 0.863
## 4 13-LOX 0.884 0.205 0.819
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.898 0.278 0.835
## 2 HSP70 0.956 0.774 0.863
## 3 PTI5 0.920 0.431 0.863
## 4 13-LOX 0.884 0.205 0.819
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 1.63 0.249
## 2 HSP70 1 6 0.530 0.494
## 3 PTI5 1 6 0.000531 0.982
## 4 13-LOX 1 6 0.652 0.450
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 1.63 0.249
## 2 HSP70 1 6 0.530 0.494
## 3 PTI5 1 6 0.000531 0.982
## 4 13-LOX 1 6 0.652 0.450
## Wilcoxon effect size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.612 BGLU2 4 4 large
## 2 measurement noninoculated inoculated 0.204 HSP70 4 4 small
## 3 measurement noninoculated inoculated 0.102 PTI5 4 4 small
## 4 measurement noninoculated inoculated 0.204 13-LOX 4 4 small
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.612 BGLU2 4 4 large
## 2 scaled noninoculated inoculated 0.204 HSP70 4 4 small
## 3 scaled noninoculated inoculated 0.102 PTI5 4 4 small
## 4 scaled noninoculated inoculated 0.204 13-LOX 4 4 small
## Cohen's d Measure of Effect Size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated -1.05 BGLU2 4 4 large
## 2 measurement noninoculated inoculated -0.798 HSP70 4 4 moderate
## 3 measurement noninoculated inoculated 0.430 PTI5 4 4 small
## 4 measurement noninoculated inoculated 0.137 13-LOX 4 4 negligible
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated -1.05 BGLU2 4 4 large
## 2 scaled noninoculated inoculated -0.798 HSP70 4 4 moderate
## 3 scaled noninoculated inoculated 0.430 PTI5 4 4 small
## 4 scaled noninoculated inoculated 0.137 13-LOX 4 4 negligible
## transcript group1 group2 p p.adj p.adj.signif
## 1 BGLU2 noninoculated inoculated 0.1142857 0.4571429 ns
## 2 HSP70 noninoculated inoculated 0.2714286 0.5428571 ns
## 3 PTI5 noninoculated inoculated 0.5857143 0.7809524 ns
## 4 13-LOX noninoculated inoculated 0.8000000 0.8000000 ns
results_15min = run_analysis_for_time(data = PS218
, time_point = "15 min"
, myorder
, pal
, groupvars
, measurevar
, y_scales1
, y_scales2
, my_dir
, plot_gene_expression
, test_and_plot
)
## Shapiro-Wilk Normality Test
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.860 0.120 0.480
## 2 HSP70 0.967 0.874 1
## 3 PTI5 0.973 0.920 1
## 4 13-LOX 0.937 0.578 1
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.860 0.120 0.480
## 2 HSP70 0.967 0.874 1
## 3 PTI5 0.973 0.920 1
## 4 13-LOX 0.937 0.578 1
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 2.83 0.143
## 2 HSP70 1 6 0.795 0.407
## 3 PTI5 1 6 0.890 0.382
## 4 13-LOX 1 6 3.67 0.104
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 2.83 0.143
## 2 HSP70 1 6 0.795 0.407
## 3 PTI5 1 6 0.890 0.382
## 4 13-LOX 1 6 3.67 0.104
## Wilcoxon effect size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.306 BGLU2 4 4 moderate
## 2 measurement noninoculated inoculated 0.816 HSP70 4 4 large
## 3 measurement noninoculated inoculated 0.408 PTI5 4 4 moderate
## 4 measurement noninoculated inoculated 0.204 13-LOX 4 4 small
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.306 BGLU2 4 4 moderate
## 2 scaled noninoculated inoculated 0.816 HSP70 4 4 large
## 3 scaled noninoculated inoculated 0.408 PTI5 4 4 moderate
## 4 scaled noninoculated inoculated 0.204 13-LOX 4 4 small
## Cohen's d Measure of Effect Size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.662 BGLU2 4 4 moderate
## 2 measurement noninoculated inoculated 2.07 HSP70 4 4 large
## 3 measurement noninoculated inoculated -0.883 PTI5 4 4 large
## 4 measurement noninoculated inoculated 0.623 13-LOX 4 4 moderate
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.662 BGLU2 4 4 moderate
## 2 scaled noninoculated inoculated 2.07 HSP70 4 4 large
## 3 scaled noninoculated inoculated -0.883 PTI5 4 4 large
## 4 scaled noninoculated inoculated 0.623 13-LOX 4 4 moderate
## transcript group1 group2 p p.adj p.adj.signif
## 1 BGLU2 noninoculated inoculated 0.30000000 0.30000000 ns
## 2 HSP70 noninoculated inoculated 0.01428571 0.05714286 ns
## 3 PTI5 noninoculated inoculated 0.22857143 0.30000000 ns
## 4 13-LOX noninoculated inoculated 0.22857143 0.30000000 ns
results_30min = run_analysis_for_time(data = PS218
, time_point = "30 min"
, myorder
, pal
, groupvars
, measurevar
, y_scales1
, y_scales2
, my_dir
, plot_gene_expression
, test_and_plot
)
## Shapiro-Wilk Normality Test
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.922 0.445 1
## 2 HSP70 0.934 0.550 1
## 3 PTI5 0.917 0.407 1
## 4 13-LOX 0.976 0.937 1
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.922 0.445 1
## 2 HSP70 0.934 0.550 1
## 3 PTI5 0.917 0.407 1
## 4 13-LOX 0.976 0.937 1
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 0.752 0.419
## 2 HSP70 1 6 1.64 0.248
## 3 PTI5 1 6 0.310 0.598
## 4 13-LOX 1 6 1.36 0.288
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 0.752 0.419
## 2 HSP70 1 6 1.64 0.248
## 3 PTI5 1 6 0.310 0.598
## 4 13-LOX 1 6 1.36 0.288
## Wilcoxon effect size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.102 BGLU2 4 4 small
## 2 measurement noninoculated inoculated 0.408 HSP70 4 4 moderate
## 3 measurement noninoculated inoculated 0.612 PTI5 4 4 large
## 4 measurement noninoculated inoculated 0.204 13-LOX 4 4 small
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.102 BGLU2 4 4 small
## 2 scaled noninoculated inoculated 0.408 HSP70 4 4 moderate
## 3 scaled noninoculated inoculated 0.612 PTI5 4 4 large
## 4 scaled noninoculated inoculated 0.204 13-LOX 4 4 small
## Cohen's d Measure of Effect Size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated -0.0596 BGLU2 4 4 negligible
## 2 measurement noninoculated inoculated -0.800 HSP70 4 4 moderate
## 3 measurement noninoculated inoculated -1.16 PTI5 4 4 large
## 4 measurement noninoculated inoculated -0.497 13-LOX 4 4 small
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated -0.0596 BGLU2 4 4 negligible
## 2 scaled noninoculated inoculated -0.800 HSP70 4 4 moderate
## 3 scaled noninoculated inoculated -1.16 PTI5 4 4 large
## 4 scaled noninoculated inoculated -0.497 13-LOX 4 4 small
## transcript group1 group2 p p.adj p.adj.signif
## 1 BGLU2 noninoculated inoculated 0.94285714 0.9428571 ns
## 2 HSP70 noninoculated inoculated 0.27142857 0.5428571 ns
## 3 PTI5 noninoculated inoculated 0.08571429 0.3428571 ns
## 4 13-LOX noninoculated inoculated 0.51428571 0.6857143 ns
y_scales1 = list(transcript == "PTI5" ~ scale_y_continuous(limits = c(0, 8), breaks = seq(0, 11, 1))
, transcript == "13-LOX" ~ scale_y_continuous(limits = c(0, 8), breaks = seq(0, 11, 1)))
y_scales2 = list(transcript == "BGLU2" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 0.5))
, transcript == "HSP70" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 0.5))
)
results_1h = run_analysis_for_time(data = PS218
, time_point = "1 h"
, myorder
, pal
, groupvars
, measurevar
, y_scales1
, y_scales2
, my_dir
, plot_gene_expression
, test_and_plot
)
## Shapiro-Wilk Normality Test
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.894 0.298 0.597
## 2 HSP70 0.808 0.0486 0.146
## 3 PTI5 0.768 0.0196 0.0782
## 4 13-LOX 0.905 0.363 0.597
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.894 0.298 0.597
## 2 HSP70 0.808 0.0486 0.146
## 3 PTI5 0.768 0.0196 0.0782
## 4 13-LOX 0.905 0.363 0.597
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 5 93.6 0.000200
## 2 HSP70 1 5 5.10 0.0736
## 3 PTI5 1 5 1.07 0.348
## 4 13-LOX 1 5 1.85 0.232
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 5 93.6 0.000200
## 2 HSP70 1 5 5.10 0.0736
## 3 PTI5 1 5 1.07 0.348
## 4 13-LOX 1 5 1.85 0.232
## Wilcoxon effect size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0 BGLU2 3 4 small
## 2 measurement noninoculated inoculated 0.401 HSP70 3 4 moderate
## 3 measurement noninoculated inoculated 0.668 PTI5 3 4 large
## 4 measurement noninoculated inoculated 0.802 13-LOX 3 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0 BGLU2 3 4 small
## 2 scaled noninoculated inoculated 0.401 HSP70 3 4 moderate
## 3 scaled noninoculated inoculated 0.668 PTI5 3 4 large
## 4 scaled noninoculated inoculated 0.802 13-LOX 3 4 large
## Cohen's d Measure of Effect Size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated -0.393 BGLU2 3 4 small
## 2 measurement noninoculated inoculated -0.912 HSP70 3 4 large
## 3 measurement noninoculated inoculated -1.35 PTI5 3 4 large
## 4 measurement noninoculated inoculated -3.09 13-LOX 3 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated -0.393 BGLU2 3 4 small
## 2 scaled noninoculated inoculated -0.912 HSP70 3 4 large
## 3 scaled noninoculated inoculated -1.35 PTI5 3 4 large
## 4 scaled noninoculated inoculated -3.09 13-LOX 3 4 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 BGLU2 noninoculated inoculated 0.40000000 0.4000000 ns
## 2 HSP70 noninoculated inoculated 0.34285714 0.4000000 ns
## 3 PTI5 noninoculated inoculated 0.07142857 0.1428571 ns
## 4 13-LOX noninoculated inoculated 0.04285714 0.1428571 ns
y_scales1 = list(transcript == "PTI5" ~ scale_y_continuous(limits = c(0, 12), breaks = seq(0, 11, 1))
, transcript == "13-LOX" ~ scale_y_continuous(limits = c(0, 12), breaks = seq(0, 11, 1)))
y_scales2 = list(transcript == "BGLU2" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 0.5))
, transcript == "HSP70" ~ scale_y_continuous(limits = c(0, 3), breaks = seq(0, 11, 0.5))
)
results_2h = run_analysis_for_time(data = PS218
, time_point = "2 h"
, myorder
, pal
, groupvars
, measurevar
, y_scales1
, y_scales2
, my_dir
, plot_gene_expression
, test_and_plot
)
## Shapiro-Wilk Normality Test
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.886 0.215 0.215
## 2 HSP70 0.760 0.0106 0.0422
## 3 PTI5 0.838 0.0720 0.144
## 4 13-LOX 0.772 0.0144 0.0431
## # A tibble: 4 × 4
## transcript W p.value p.adj
## <fct> <dbl> <dbl> <dbl>
## 1 BGLU2 0.886 0.215 0.215
## 2 HSP70 0.760 0.0106 0.0422
## 3 PTI5 0.838 0.0720 0.144
## 4 13-LOX 0.772 0.0144 0.0431
## Quantile-Quantile plots
## Levene's test for homogeneity of variance across groups
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 0.151 0.711
## 2 HSP70 1 6 0.926 0.373
## 3 PTI5 1 6 2.14 0.194
## 4 13-LOX 1 6 0.634 0.456
## # A tibble: 4 × 5
## transcript df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 BGLU2 1 6 0.151 0.711
## 2 HSP70 1 6 0.926 0.373
## 3 PTI5 1 6 2.14 0.194
## 4 13-LOX 1 6 0.634 0.456
## Wilcoxon effect size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.102 BGLU2 4 4 small
## 2 measurement noninoculated inoculated 0 HSP70 4 4 small
## 3 measurement noninoculated inoculated 0.816 PTI5 4 4 large
## 4 measurement noninoculated inoculated 0.816 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.102 BGLU2 4 4 small
## 2 scaled noninoculated inoculated 0 HSP70 4 4 small
## 3 scaled noninoculated inoculated 0.816 PTI5 4 4 large
## 4 scaled noninoculated inoculated 0.816 13-LOX 4 4 large
## Cohen's d Measure of Effect Size
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 measurement noninoculated inoculated 0.413 BGLU2 4 4 small
## 2 measurement noninoculated inoculated -0.442 HSP70 4 4 small
## 3 measurement noninoculated inoculated -2.37 PTI5 4 4 large
## 4 measurement noninoculated inoculated -12.6 13-LOX 4 4 large
## # A tibble: 4 × 8
## .y. group1 group2 effsize transcript n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <fct> <int> <int> <ord>
## 1 scaled noninoculated inoculated 0.413 BGLU2 4 4 small
## 2 scaled noninoculated inoculated -0.442 HSP70 4 4 small
## 3 scaled noninoculated inoculated -2.37 PTI5 4 4 large
## 4 scaled noninoculated inoculated -12.6 13-LOX 4 4 large
## transcript group1 group2 p p.adj p.adj.signif
## 1 BGLU2 noninoculated inoculated 0.67142857 0.81428571 ns
## 2 HSP70 noninoculated inoculated 0.81428571 0.81428571 ns
## 3 PTI5 noninoculated inoculated 0.01428571 0.02857143 *
## 4 13-LOX noninoculated inoculated 0.01428571 0.02857143 *
## R version 4.4.1 (2024-06-14 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26100)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8
## [2] LC_CTYPE=English_United Kingdom.utf8
## [3] LC_MONETARY=English_United Kingdom.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.utf8
##
## time zone: Europe/Ljubljana
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.5.2 magrittr_2.0.3
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 libcoin_1.0-10 dplyr_1.1.4
## [4] farver_2.1.2 fastmap_1.2.0 TH.data_1.1-3
## [7] MKdescr_0.8 rpart_4.1.24 digest_0.6.37
## [10] lifecycle_1.0.4 arrangements_1.1.9 survival_3.8-3
## [13] compiler_4.4.1 rlang_1.1.5 sass_0.4.10
## [16] tools_4.4.1 utf8_1.2.5 yaml_2.3.10
## [19] data.table_1.17.0 knitr_1.50 ggsignif_0.6.4
## [22] labeling_0.4.3 RColorBrewer_1.1-3 multcomp_1.4-28
## [25] abind_1.4-8 MKinfer_1.2 withr_3.0.2
## [28] purrr_1.0.4 ggh4x_0.3.0 nnet_7.3-20
## [31] grid_4.4.1 stats4_4.4.1 ggpubr_0.6.0
## [34] jomo_2.7-6 mice_3.17.0 scales_1.4.0
## [37] iterators_1.0.14 MASS_7.3-64 dichromat_2.0-0.1
## [40] cli_3.6.3 mvtnorm_1.3-3 rmarkdown_2.29
## [43] crayon_1.5.3 reformulas_0.4.1 generics_0.1.4
## [46] exactRankTests_0.8-35 rstudioapi_0.17.1 DBI_1.2.3
## [49] minqa_1.2.8 cachem_1.1.0 modeltools_0.2-24
## [52] splines_4.4.1 parallel_4.4.1 mitools_2.4
## [55] matrixStats_1.5.0 vctrs_0.6.5 boot_1.3-31
## [58] glmnet_4.1-8 Matrix_1.7-1 sandwich_3.1-1
## [61] jsonlite_2.0.0 carData_3.0-5 car_3.1-3
## [64] mitml_0.4-5 rstatix_0.7.2 Formula_1.2-5
## [67] foreach_1.5.2 tidyr_1.3.1 jquerylib_0.1.4
## [70] glue_1.8.0 pan_1.9 nloptr_2.2.1
## [73] codetools_0.2-20 cowplot_1.1.3 stringi_1.8.7
## [76] gtable_0.3.6 shape_1.4.6.1 lme4_1.1-37
## [79] gmp_0.7-5 tibble_3.2.1 pillar_1.10.2
## [82] htmltools_0.5.8.1 miceadds_3.17-44 R6_2.6.1
## [85] Rdpack_2.6.4 evaluate_1.0.3 lattice_0.22-6
## [88] rbibutils_2.3 backports_1.5.0 openxlsx_4.2.8
## [91] broom_1.0.8 bslib_0.9.0 Rcpp_1.0.14
## [94] zip_2.3.2 nlme_3.1-166 coin_1.4-3
## [97] xfun_0.52 zoo_1.8-14 pkgconfig_2.0.3